Monitoring Kubernetes clusters on AWS, GCP and Azure using Prometheus Operator by CoreOS

Note: the work on this repository is now based on CoreOS's kube-prometheus and it will be the default option for Kubernetes 1.7.X and up. For 1.5.X and 1.6.X you can deploy a simpler solution, located in ./basic directory. The purpose of this project is to provide a simple and interactive method to deploy and configure Prometheus on Kubernetes, especially for the users that are not using Helm.

Features

Prometheus Operator with support for Prometheus v2.X.X
highly available Prometheus and Alertmaneger
InCluster deployment using StatefulSets for persistent storage
auto-discovery for services and pods
automatic RBAC configuration
preconfigured alerts
preconfigured Grafana dashboards
easy to setup; usually less than a minute to deploy a complete monitoring solution for Kubernetes
support for Kubernetes v1.7.x and up running in AWS, GCP and Azure
tested on clusters deployed using kube-aws, kops, GKE and Azure

One minute deployment

Prerequisites

Kubernetes cluster and kubectl configured
Security Groups configured to allow the following ports:
- 9100/TCP - node-exporter
- 10250/TCP - kubernetes nodes metrics,
- 10251/TCP - kube-scheduler
- 10252/TCP - kube-controller-manager
- 10054/TCP and 10055/TCP - kube-dns

Optional

SMTP Account for email alerts
Token for Slack alerts

Pre-Deployment

Clone the repository and checkout the latest release: curl -L https://git.io/getPrometheusKubernetes | sh -

Custom settings

All the components versions can be configured using the interactive deployment script. Same for the SMTP account or the Slack token.

Some other settings that can be changed before deployment:

Prometheus replicas: default 2 ==> manifests/prometheus/prometheus-k8s.yaml
persistent volume size: default 40Gi ==> manifests/prometheus/prometheus-k8s.yaml
allocated memory for Prometheus pods: default 2Gi ==> manifests/prometheus/prometheus-k8s.yaml
Alertmanager replicas: default 3 ==> manifests/alertmanager/alertmanager.yaml
Alertmanager configuration: ==> assets/alertmanager/alertmanager.yaml
custom Grafana dashboards: add yours in assets/grafana/ with names ending in -dashboard.json
custom alert rules: ==> assets/prometheus/rules/

Note: please commit your changes before deployment if you wish to keep them. The deploy script will remove the changes on most of the files.

Deploy

./deploy

Now you can access the dashboards locally using kubectl port-forwardcommand, or expose the services using a ingress or a LoadBalancer. Please check the ./tools directory to quickly configure a ingress or proxy the services to localhost.

To remove everything, just execute the ./teardown script.

Updating configurations

update alert rules: add or change the rules in assets/prometheus/rules/ and execute scripts/generate-rules-configmap.sh. Then apply the changes using kubectl apply -f manifests/prometheus/prometheus-k8s-rules.yaml -n monitoring
update grafana dashboards: add or change the existing dashboards in assets/grafana/ and execute scripts/generate-dashboards-configmap.sh. Then apply the changes using kubectl apply -f manifests/grafana/grafana-dashboards.cm.yaml.

Note: all the Grafana dashboards should have names ending in -dashboard.json.

Custom Prometheus configuration

The official documentation for Prometheus Operator custom configuration can be found here: custom-configuration.md If you wish, you can update the Prometheus configuration using the ./tools/custom-configuration/update_config script.

borg286/prometheus-kubernetes